From b2f9168a571eaa6a9e1d0f03eb9362f4abd00694 Mon Sep 17 00:00:00 2001
From: yanglbme <szuyanglb@outlook.com>
Date: Wed, 26 Dec 2018 20:40:31 +0800
Subject: [PATCH] docs: add hystrix-thread-pool-isolation.md
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Hystrix 线程池技术实现资源隔离
Redis 小修改
---
 README.md                                     |   1 +
 .../hystrix-thread-pool-isolation.md          | 116 ++++++++++++++++++
 .../img/hystrix-thread-pool-isolation.png     | Bin 0 -> 12597 bytes
 docs/high-concurrency/redis-consistence.md    |   2 +-
 .../redis-expiration-policies-and-lru.md      |   6 +-
 img/hystrix-thread-pool-isolation.png         | Bin 0 -> 12597 bytes
 6 files changed, 121 insertions(+), 4 deletions(-)
 create mode 100644 docs/high-availability/hystrix-thread-pool-isolation.md
 create mode 100644 docs/high-availability/img/hystrix-thread-pool-isolation.png
 create mode 100644 img/hystrix-thread-pool-isolation.png

diff --git a/README.md b/README.md
index 940788b..3c86cce 100644
--- a/README.md
+++ b/README.md
@@ -82,6 +82,7 @@
 ## 高可用架构
 - [Hystrix 介绍](/docs/high-availability/hystrix-introduction.md)
 - [电商网站详情页系统架构](/docs/high-availability/e-commerce-website-detail-page-architecture.md)
+- [Hystrix 线程池技术实现资源隔离](/docs/high-availability/hystrix-thread-pool-isolation.md)
 
 ### 高可用系统
 - 如何设计一个高可用系统？
diff --git a/docs/high-availability/hystrix-thread-pool-isolation.md b/docs/high-availability/hystrix-thread-pool-isolation.md
new file mode 100644
index 0000000..84a7357
--- /dev/null
+++ b/docs/high-availability/hystrix-thread-pool-isolation.md
@@ -0,0 +1,116 @@
+## 基于 Hystrix 线程池技术实现资源隔离
+
+上一讲提到，如果从 Nginx 开始，缓存都失效了，Nginx 会直接通过缓存服务调用商品服务获取最新商品数据（我们基于电商项目做个讨论），有可能出现调用延时而把缓存服务资源耗尽的情况。这里，我们就来说说，怎么通过 Hystrix 线程池技术实现资源隔离。
+
+资源隔离，就是说，你如果要把对某一个依赖服务的所有调用请求，全部隔离在同一份资源池内，不会去用其它资源了，这就叫资源隔离。哪怕对这个依赖服务，比如说商品服务，现在同时发起的调用量已经到了 1000，但是线程池内就 10 个线程，最多就只会用这 10 个线程去执行，不会说，对商品服务的请求，因为接口调用延时，将 tomcat 内部所有的线程资源全部耗尽。
+
+Hystrix 进行资源隔离，其实是提供了一个抽象，叫做 command。这也是 Hystrix 最最基本的资源隔离技术。
+
+### 利用 HystrixCommand 获取单条数据
+我们通过将调用商品服务的操作封装在 HystrixCommand 中，限定一个 key，比如下面的 `GetProductInfoCommandGroup`，在这里我们可以简单认为这是一个线程池，每次调用商品服务，就只会用该线程池中的资源，不会再去用其它线程资源了。
+
+```java
+public class GetProductInfoCommand extends HystrixCommand<ProductInfo> {
+
+    private Long productId;
+
+    public GetProductInfoCommand(Long productId) {
+        super(HystrixCommandGroupKey.Factory.asKey("GetProductInfoCommandGroup"));
+        this.productId = productId;
+    }
+
+    @Override
+    protected ProductInfo run() {
+        String url = "http://localhost:8081/getProductInfo?productId=" + productId;
+        // 调用商品服务接口
+        String response = HttpClientUtils.sendGetRequest(url);
+        return JSONObject.parseObject(response, ProductInfo.class);
+    }
+}
+```
+
+我们在缓存服务接口中，根据 productId 创建 command 并执行，获取到商品数据。
+
+```java
+@RequestMapping("/getProductInfo")
+@ResponseBody
+public String getProductInfo(Long productId) {
+    HystrixCommand<ProductInfo> getProductInfoCommand = new GetProductInfoCommand(productId);
+    
+    // 通过command执行，获取最新商品数据
+    ProductInfo productInfo = getProductInfoCommand.execute();
+    System.out.println(productInfo);
+    return "success";
+}
+```
+
+上面执行的是 execute() 方法，其实是同步的。也可以对 command 调用 queue() 方法，它仅仅是将 command 放入线程池的一个等待队列，就立即返回，拿到一个 Future 对象，后面可以继续做其它一些事情，然后过一段时间对 Future 调用 get() 方法获取数据。这是异步的。
+
+### 利用 HystrixObservableCommand 批量获取数据
+只要是获取商品数据，全部都绑定到同一个线程池里面去，我们通过 HystrixObservableCommand 的一个线程去执行，而在这个线程里面，批量把多个 productId 的 productInfo 拉回来。
+
+```java
+public class GetProductInfosCommand extends HystrixObservableCommand<ProductInfo> {
+
+    private String[] productIds;
+
+    public GetProductInfosCommand(String[] productIds) {
+        // 还是绑定在同一个线程池
+        super(HystrixCommandGroupKey.Factory.asKey("GetProductInfoGroup"));
+        this.productIds = productIds;
+    }
+
+    @Override
+    protected Observable<ProductInfo> construct() {
+        return Observable.unsafeCreate((Observable.OnSubscribe<ProductInfo>) subscriber -> {
+
+            for (String productId : productIds) {
+                // 批量获取商品数据
+                String url = "http://localhost:8081/getProductInfo?productId=" + productId;
+                String response = HttpClientUtils.sendGetRequest(url);
+                ProductInfo productInfo = JSONObject.parseObject(response, ProductInfo.class);
+                subscriber.onNext(productInfo);
+            }
+            subscriber.onCompleted();
+
+        }).subscribeOn(Schedulers.io());
+    }
+}
+```
+
+在缓存服务接口中，根据传来的 id 列表，比如是以 `,` 分隔的 id 串，通过上面的 HystrixObservableCommand，执行 Hystrix 的一些 API 方法，获取到所有商品数据。
+```java
+public String getProductInfos(String productIds) {
+    String[] productIdArray = productIds.split(",");
+    HystrixObservableCommand<ProductInfo> getProductInfosCommand = new GetProductInfosCommand(productIdArray);
+    Observable<ProductInfo> observable = getProductInfosCommand.observe();
+
+    observable.subscribe(new Observer<ProductInfo>() {
+        @Override
+        public void onCompleted() {
+            System.out.println("获取完了所有的商品数据");
+        }
+
+        @Override
+        public void onError(Throwable e) {
+            e.printStackTrace();
+        }
+
+        /**
+         * 获取完一条数据，就回调一次这个方法
+         * @param productInfo
+         */
+        @Override
+        public void onNext(ProductInfo productInfo) {
+            System.out.println(productInfo);
+        }
+    });
+    return "success";
+}
+```
+
+我们回过头来，看看 Hystrix 线程池技术是如何实现资源隔离的。
+
+![hystrix-thread-pool-isolation](/img/hystrix-thread-pool-isolation.png)
+
+从 Nginx 开始，缓存都失效了，那么 Nginx 通过缓存服务去调用商品服务。缓存服务默认的线程大小是 10 个，最多就只有 10 个线程去调用商品服务的接口。即使商品服务接口故障了，最多就只有 10 个线程会 hang 死在调用商品服务接口的路上，缓存服务的 tomcat 内其它的线程还是可以用来调用其它的服务，干其它的事情。
\ No newline at end of file
diff --git a/docs/high-availability/img/hystrix-thread-pool-isolation.png b/docs/high-availability/img/hystrix-thread-pool-isolation.png
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diff --git a/docs/high-concurrency/redis-consistence.md b/docs/high-concurrency/redis-consistence.md
index e1bddc6..f8a3f0b 100644
--- a/docs/high-concurrency/redis-consistence.md
+++ b/docs/high-concurrency/redis-consistence.md
@@ -22,7 +22,7 @@
 
 另外更新缓存的代价有时候是很高的。是不是说，每次修改数据库的时候，都一定要将其对应的缓存更新一份？也许有的场景是这样，但是对于**比较复杂的缓存数据计算的场景**，就不是这样了。如果你频繁修改一个缓存涉及的多个表，缓存也频繁更新。但是问题在于，**这个缓存到底会不会被频繁访问到？**
 
-举个栗子，一个缓存涉及的表的字段，在 1 分钟内就修改了 20 次，或者是 100 次，那么缓存更新 20 次，100 次；但是这个缓存在 1 分钟内只被读取了 1 次，有**大量的冷数据**。实际上，如果你只是删除缓存的话，那么在 1 分钟内，这个缓存不过就重新计算一次而已，开销大幅度降低。**用到缓存才去算缓存。**
+举个栗子，一个缓存涉及的表的字段，在 1 分钟内就修改了 20 次，或者是 100 次，那么缓存更新 20 次、100 次；但是这个缓存在 1 分钟内只被读取了 1 次，有**大量的冷数据**。实际上，如果你只是删除缓存的话，那么在 1 分钟内，这个缓存不过就重新计算一次而已，开销大幅度降低。**用到缓存才去算缓存。**
 
 其实删除缓存，而不是更新缓存，就是一个 lazy 计算的思想，不要每次都重新做复杂的计算，不管它会不会用到，而是让它到需要被使用的时候再重新计算。像 mybatis，hibernate，都有懒加载思想。查询一个部门，部门带了一个员工的 list，没有必要说每次查询部门，都里面的 1000 个员工的数据也同时查出来啊。80% 的情况，查这个部门，就只是要访问这个部门的信息就可以了。先查部门，同时要访问里面的员工，那么这个时候只有在你要访问里面的员工的时候，才会去数据库里面查询 1000 个员工。
 
diff --git a/docs/high-concurrency/redis-expiration-policies-and-lru.md b/docs/high-concurrency/redis-expiration-policies-and-lru.md
index 38fb3fe..9fea56c 100644
--- a/docs/high-concurrency/redis-expiration-policies-and-lru.md
+++ b/docs/high-concurrency/redis-expiration-policies-and-lru.md
@@ -27,7 +27,7 @@ redis 过期策略是：**定期删除+惰性删除**。
 
 但是问题是，定期删除可能会导致很多过期 key 到了时间并没有被删除掉，那咋整呢？所以就是惰性删除了。这就是说，在你获取某个 key 的时候，redis 会检查一下 ，这个 key 如果设置了过期时间那么是否过期了？如果过期了此时就会删除，不会给你返回任何东西。
 
-> 获取key 的时候，如果此时 key 已经过期，就删除，不会返回任何东西。
+> 获取 key 的时候，如果此时 key 已经过期，就删除，不会返回任何东西。
 
 但是实际上这还是有问题的，如果定期删除漏掉了很多过期 key，然后你也没及时去查，也就没走惰性删除，此时会怎么样？如果大量过期 key 堆积在内存里，导致 redis 内存块耗尽了，咋整？
 
@@ -36,9 +36,9 @@ redis 过期策略是：**定期删除+惰性删除**。
 ### 内存淘汰机制
 redis 内存淘汰机制有以下几个：
 - noeviction: 当内存不足以容纳新写入数据时，新写入操作会报错，这个一般没人用吧，实在是太恶心了。
-- **allkeys-lru**：当内存不足以容纳新写入数据时，在**键空间**中，移除最近最少使用的 key（这个是**最常用**的）
+- **allkeys-lru**：当内存不足以容纳新写入数据时，在**键空间**中，移除最近最少使用的 key（这个是**最常用**的）。
 - allkeys-random：当内存不足以容纳新写入数据时，在**键空间**中，随机移除某个 key，这个一般没人用吧，为啥要随机，肯定是把最近最少使用的 key 给干掉啊。
-- volatile-lru：当内存不足以容纳新写入数据时，在**设置了过期时间的键空间**中，移除最近最少使用的 key（这个一般不太合适）
+- volatile-lru：当内存不足以容纳新写入数据时，在**设置了过期时间的键空间**中，移除最近最少使用的 key（这个一般不太合适）。
 - volatile-random：当内存不足以容纳新写入数据时，在**设置了过期时间的键空间**中，**随机移除**某个 key。
 - volatile-ttl：当内存不足以容纳新写入数据时，在**设置了过期时间的键空间**中，有**更早过期时间**的 key 优先移除。
 
diff --git a/img/hystrix-thread-pool-isolation.png b/img/hystrix-thread-pool-isolation.png
new file mode 100644
index 0000000000000000000000000000000000000000..d51983ba5412c6623d9cc671cd811afc77c35111
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